1. Field of the Invention
The present invention relates generally to the field digital telecommunication, and more particularly to an adaptive equalization method for eliminating echoes at the receiver of a communication channel.
2. Description of the Related Art
Many systems utilize wireless transmission of signals, including wireless telephone systems, wireless television systems, and other systems which perform wireless communication. Such systems generally include at least one transmitter which transmits signals through a channel to at least one receiver. During transmission of the signals through the channel, multiple reflections and diffraction from natural and/or man-made obstacles can occur. Such obstacles can include buildings, homes, vehicles, or natural terrain such as mountains or trees. The reflections and diffraction from these objects can create multi-path distortion of the transmitted signal. In open wireless channels, multipath reflections can introduce inter-symbol interference ISI into the received signal. In certain wired transmission systems, multipath reflections can also occur, e.g., micro-reflections due to impedance mismatch from various passive or active elements in the channel, such as taps, amplifiers, and cables, etc.
An equalizer is an electrical circuit positioned after signal down-conversion and before error-correction. The equalizer processes a signal to remove distortions introduced by the channel, for example, echoes/ghosts introduced by multiple paths in a radio/television channel. The signal output of the equalizer is “better” the more closely it approximates the signal input to the transmitter. Signals can be analog or digital. Digital signals may have many possible levels of amplitude/phases. Unique combinations of amplitude/phase are called symbols, and a digital signal may be represented as a symbol stream. For example, the North American High Definition Television (HDTV) Standard utilizes 8 amplitude levels with a DC offset. The transmitter produces one sideband with 8 amplitude levels and a vestigial carrier (8-VSB).
The equalizer in a radio/TV receiver is designed to compensate for distortions of the signal introduced by the channel. Typical distortions in a TV channel come from reflections from buildings and aircraft. Also, persons moving near an antenna can alter the relative strengths of individual reflection paths striking an antenna. Thus there is a need to compensate for echo ensembles which may be static or a mix of static and dynamic paths. For HDTV the symbol period is about 0.093 μsec and the echoes to be compensated can be up to 60 μsec away. Thus there can be a high degree of inter-symbol interference (ISI), up to 645 symbol periods away. Moving reflectors introduce Doppler shifts. The channel may also introduce noise into the signal, where the noise may be white or impulsive (bursting). For digital signals, noise can cause decision circuits to make symbol errors. The processing after the equalizer is designed to remove symbol errors through the use of trellis decoding, de-interleaving, and error correction (Reed-Solomon decoder in FIG. 2). Randomizing a signal breaks up long strings of a single symbol, which neutralizes the effect of long strings of DC offsets in AC-coupled circuits. For HDTV, the symbol error rate at the input to the trellis decoder can be up to about 20% before the error correction processing breaks down.
Typically, an equalizer is a filter with a characteristic response of its outputs to its inputs. The filter characteristic may be altered to compensate for specific distortions introduced by a specific channel. Ideally, the equalizer response compensates for all the distortions introduced by the channel. However, the more complex the distortions, the more complex the compensation required, ultimately requiring a larger equalizer circuit. For static distortions, the measure of compensation quality is the mean square error (MSE) across a long string of random symbols, plus any noise introduced by the equalizer as it fluctuates about the converged characteristic. For dynamic distortions the measure of quality includes a tracking component, such as the rate of convergence, or the allowable Doppler shift.
Various filter architectures have been used or proposed for digital signal equalizers. Filter architectures may be described by the features included in the signal data path and by the features in the control algorithm. The data path input and output are symbol streams in the time domain. Conventional digital filters keep the data in sequence in the time domain without explicit reference to frequency. However, the input symbol stream may be transformed to the frequency domain by means of a discrete Fourier transform (DFT), altered in the frequency domain, and then returned to the time domain by means of an inverse discrete Fourier transform (IDFT).
Frequency domain filters have been proposed for use in modems to receive high-bit rate data across digital subscriber lines (DSL) made of twisted pair cable, see Polley et al. U.S. Pat. No. 5,809,069. In DSL modem applications, the symbol rate is allowed to vary depending on line quality, however the maximum symbol rate is limited by the receiver's analog sampling rate, currently at about 2.2 M samples per second. Two-way cross talk and multiple echoes are the main distortions for DSL, with Doppler shifts non-existent. Since the DSL cable does not change very often, a complex training sequence may be used to determine equalizer characteristics, and then no further update is needed, perhaps for many days. These are very different requirements than for radio/television channels. Thus equalizers designed for DSL generally would not work for radio/television, especially for HDTV where the symbol rate is higher, about 10.8 M symbols per sec, and the channel fluctuates sometimes up to tens of Hertz.
Frequency domain digital equalizers have not been proposed for HDTV or other high-symbol-rate broadcast links. One reason is that frequency domain digital equalizers have a higher computational complexity, which in the past would have produced excessive receiver cost. Thus control algorithms for frequency-domain HDTV equalizers are in a state of infancy compared to the prior art for time-domain equalizers. Many of the features used or proposed for HDTV time-domain equalizers may be applicable to frequency domain equalizers, but may not be necessary or desired. A brief summary of time domain equalizers follows, with a focus on their reported limitations.
Most digital filters are of the type Finite Impulse Response (FIR) or infinite impulse response (IIR). A FIR filter can be constructed of a tapped delay line and a summation node where each input to the summation is a tap signal multiplied by an independent coefficient (or “weight”) for that tap. The more taps in the filter, the more capable it is to compensate for longer echo delays. An IIR filter can be formed by the combination of a component FIR filter and a digital adder. The adder sums the inputs to the IIR with the outputs of its component FIR filter and supplies IIR outputs, which outputs are also fed back to the input of the component FIR filter. Because of the feedback, IIR filters can cancel longer delayed echoes better than FIR filters, however, their stability is not assured.
The characteristics of a transmitted digital signal are generally known a priori. Therefore, at least in theory, it is possible to utilize such characteristics in a system of multipath detection and adaptive channel equalization. However, this approach to channel equalization has various problems. Accordingly, some signal communication standards utilize a training signal for the detection and characterization of multipath distortion. For example, television signal transmission systems recurrently transmit a training signal situated in a portion of the TV signal that is currently unused for video purposes, and this training signal is used for the detection and characterization of multipath distortion. Here it is presumed that the transmitted training signal will suffer the same multipath distortions as the rest of the television signal. The receiver can then examine the distorted training signal that is received and, with a priori knowledge of the distortion-free training signal, can calculate the characteristics of the transmission channel. The receiver can then calculate the characteristics required of a filter that will respond to the received signal, but will suppress the effects of multipath signals. A variety of different types of training or “ghost cancellation reference” signals have been described in patents and other technical publications.
The following is a quote from U.S. Pat. No. 5,648,987 to Yang, et al., issued Jul. 15, 1997, pp. 18-19, columns 2-3:
“In the digital television signals for broadcasting high-definition television (HDTV), each data field contains 313 data lines, and the fields are consecutively numbered modulo-two in order of their occurrence. Each line of data starts with a line synchronization code group of four symbols having successive values of +S, −S, −S and +S. The value +S is one level below the maximum positive data excursion, and the value −S is one level above the maximum negative data excursion. The lines of data are each of 77.3 microsecond duration, and there are 832 symbols per data line for a symbol rate of about 10 [megasymbols/second]. The initial line of each data field is a field synchronization code group that codes a training signal for channel-equalization and multipath signal suppression procedures. The training signal is a 511-sample pseudo-random sequence (or “PR-sequence”) followed by three 63-sample PR sequences. This training signal is transmitted in accordance with a first logic convention in the first line of each odd-numbered data field and in accordance with a second logic convention in the first line of each even-numbered data field, the first and second logic conventions being one's complementary respective to each other. The reference sequence(s) can be analyzed, channel characterization determined and appropriate equalizing filter can be implemented. However, this process can be rather slow and is definitely not suitable for any multipath signal, such as some airplane flutter, that varies quite quickly with elapsed time.
Owing to the nature of the digital signal used in HDTV, the adaptation of the channel-equalization filter could be performed with every received symbol on a decision-directed basis (in the absence of the reference sequence). However, currently the limiting factor on the speed of initially equalizing the reception channel or of tracking a time-varying multipath is established by the processing speeds of the computing devices being utilized. Increasing the processing speeds of the computing devices will improve system performance until the point is reached at which all the computations and the subsequent updating of the filter coefficients can be realized with each newly received symbol or with a reasonably small group of newly received symbols.
Several methods of performing “adaptive equalization/multipath cancellation” are described in the literature. In simplest terms, the input signal is processed through an equalizer filter. The filter output, is “compared” to the desired output and based on a certain algorithm a correction to the filter parameters is computed and adapted to the filter. The process is continuously repeated until the equalized filter output is “correct”, so multipath effects are attenuated sufficiently that they do not exceed levels prescribed as being “acceptable”. To aid in developing an understanding of the nature of the computations involved, the reader is referred to the following publications, which are hereby incorporated by reference:
G. A. Clark, S. K. Mitra, S. R. Parker, “Block implementation of adaptive digital filters,” IEEE Trans. ASSP, pp. 744-752, Vol. 29, June 1981; and
J. C. Lee and C. K. Un, “Performance Analysis of Frequency-Domain Block LMS Adaptive Digital Filters,” IEEE Trans. on Circuits and Systems, pp. 173-189, Vol. 36, No. 2, February 1989.
The basic adaptive equalization/multipath cancellation equations are known from the last-listed of these references to be:
                                          y            n                    =                                    ∑                              k                =                0                                            N                -                1                                      ⁢                                          W                k                m                            ·                              X                                  (                                      n                    -                    k                                    )                                                                    ,                                  ⁢                  k          =          0                ,        1        ,        …        ⁢                                  ,                  (                      N            -            1                    )                ,                              and            ⁢                                                  ⁢            mN                    ≤          n          <                                    (                              m                +                1                            )                        ⁢            N                                              (        1        )                                          ⅇ                                                            ⁢            n                          =                              y                                                                      ⁢              n                                -                      d                                                                      ⁢              n                                                          (        2        )                                          Δ          k          m                =                              ∑                          j              =              mN                                                      [                                                      (                                          m                      +                      1                                        )                                    ⁢                  N                                ]                            -              1                                ⁢                                    ⅇ              j                        ·                          X                              (                                  j                  -                  k                                )                                                                        (        3        )                                          W          k                      (                          m              +              1                        )                          =                              W            k            m                    +                      μ            ·                          Δ              k              m                                                          (        4        )            
This adaptation algorithm is based on a group of N symbols and not on each symbol. Such an algorithm is identified as “Block LMS”. It is known to have the same performance as the well-known LMS (least mean squares) algorithm when the channel varying speed is slower than the realized convergence with the block of N symbols. (Superscripted terms in these equations are not terms raised to “powers” indicated by the superscript. Rather the superscripts following general terms are a set of further indices for sets of specific terms, the specific terms in each set being indexed by subscripts following general terms.) A channel-equalization filter with coefficients Wk (the parameter m is not shown here since it only indicates the number of updates) and input data Xn (ghosted and/or equalization needed) generates equalized data yn according to equation (1). Since the equalization indicated by equation (1) must be done in real-time, standard practice is to implement that equalization using an appropriate FIR filter. When equalization is done using a training signal, an IIR filter suppresses multipath responses that are delayed respective to strongest signal better than an FIR filter having the same number of taps. In decision-directed equalization, the computation of weighting coefficients for the channel-equalization filter is based strictly on some observation that does not depend on or indicate the time relationship of multipath signals. When the computation procedure begins without knowledge of suitable initial values of the weighting coefficients, the procedure is referred to as “blind” equalization. Because the response of an IIR filter is regenerative in nature, errors introduced by “blind” equalization tend to be perpetuated and will be rarely eliminated by continuing calculation. Presumably this is the reason that, until the invention [U.S. Pat. No. 5,648,987] was made, decision-directed equalization had invariably been used only with FIR channel-equalization filters.”
As described in U.S. Pat. No. 5,648,987 to Yang et al., equalization filters are known which cascade a finite-impulse-response (FIR) filter with an infinite-impulse-response (IIR) filter. The IIR filter can be formed from a digital adder with a component FIR filter. The coefficients of the component FIR filter in the IIR filter can be initially adjusted in response to information obtained from the training signals contained in portions of the transmitted data. This initial adjustment of the coefficients of the component FIR filter is performed to avoid the instability problems normally associated with IIR filters. Thereafter, Yang teaches that the coefficients of the component FIR filter can be computed as described in Yang using a further FIR filter to implement decision directed techniques in which best estimates of correct filter response are formed by quantizing actual filter response. Yang further teaches that, in equalization filters which cascade a finite-impulse-response (FIR) filter with the infinite-impulse-response (IIR) filter, the coefficients of the filters are independently adjusted.
The history of time-domain equalizers for North American HDTV is surveyed by M. Ghosh [1], who shows that the 8-VSB modulation was selected in part because of more effective equalization than competitive schemes. “The equalizer in the prototype built by Zenith was a DFE [Decision Feedback Equalizer] with 64 forward and 192 feedback taps [for the two digital filters] and was adapted using the standard LMS algorithm on the pseudo random noise (PN) sequence in the field sync segment. Since the field-sync segment arrives only once every field (i.e., about once every 24 ms), the overall rate of convergence of the equalizer can be quite slow.” However, after convergence on a static echo pattern, the mean square error (MSE) was fairly good, and this helped 8-VSB to win the Grand Alliance recommendation. Testing on dynamic echoes was very limited at the time. HDTV manufacturers are free however to use any equalizer they like as long as it meets performance goals.
The summary from M. Ghosh in 1998 [1]:
“ . . . the need for tracking time-varying channels indicates the use of blind algorithms. In this paper the advantages of a blind DFE structure were presented via the ATTC [Advanced Television Test Center] test results, as well as simulations with the Godard blind equalization algorithm. The advantages of using a blind DFE have since been validated by numerous field tests that have been conducted by HDTV receiver manufacturers. In addition to the advantages of the blind DFE in long multipath and dynamic multipath, a blind algorithm enables faster acquisition as well. With trained-only equalization it may take the equalizer 10-15 data fields to converge since the field sync occurs only once in each field. However, with blind equalization the equalizer converges in less than one field in most cases.”
“One of the main concerns in implementing an equalizer for digital television receivers is the number of taps required, which is on the order of 256. Hence, simple equalization algorithms can greatly reduce the hardware required to implement such a long equalizer. Future work in this area needs to concentrate on developing hardware efficient blind algorithms. The Godard algorithm and the RCA [Reduced Constellation Algorithm] both require multipliers in each of the tap update steps, which can add up to a large area requirement in silicon. Recent work in this area [1 [20]-[22]] has concentrated on sign-error versions of the Godard cost function, which does away with the need for multiplications in the tap update step and reduces implementation complexity. Faster algorithms with low complexity are also an area in need of further research, since the Godard algorithm can be quite slow in tracking rapidly varying channels.”
“Finally, while the literature on the analyses of blind algorithms for linear equalizers is very rich, the same is not true for blind algorithms for DFE's with significant error propagation. HDTV receivers happen to be a very important commercial application for such structures, and there is a need for more analytical results in this area in order to prove conclusively some of the simulation results described in Section V. For now, most receiver manufacturers must rely heavily on simulation results of blind DFE's in order to design high-performance receivers for the real-world scenario of low SNR and long, possibly dynamic, multipath channels.”
In summary the prior art for HDTV equalizers suffers from:                1. Slow convergence when using the PN sequence for training, and therefore an inability to track Doppler shifts or other dynamic multipath distortion.        2. Poor MSE (noisy) in converged state if a bigger step is used for faster convergence, although step size may be varied (Godard Algorithm).        3. Poor ability to center on the strongest path signal and cancel pre-echoes.        4. Limited range of echo delays due to steep cost rise to get beyond 200 taps.        5. Since a multiply is required for each tap, a large number of multipliers are needed for long delays, adding power consumption.        6. Stability issues when using IIR filters.        
Accordingly, some objectives of embodiments of the present invention are to mitigate the above problems of time domain equalizers.